It is common in the field of medicine to perform visual examination to diagnose disease. For example, visual examination of the cervix can discern areas where there is a suspicion of pathology. However, direct visual observation alone may be inadequate for proper identification of an abnormal tissue sample, particularly in the early stages of disease.
In some procedures, such as colposcopic examinations, a chemical agent, such as acetic acid, is applied to enhance the differences in appearance between normal and pathological tissue. Such acetowhitening techniques may aid a colposcopist in the determination of areas in which there is a suspicion of pathology.
Colposcopic techniques are not perfect. They generally require analysis by a highly-trained physician. Colposcopic images may contain complex and confusing patterns and may be affected by glare, shadow, or the presence of blood or other obstruction, rendering an indeterminate diagnosis.
Spectral analysis has increasingly been used to diagnose disease in tissue. Spectral analysis is based on the principle that the intensity of light that is transmitted from an illuminated tissue sample may indicate the state of health of the tissue. As in colposcopic examination, spectral analysis of tissue may be conducted using a contrast agent such as acetic acid. In spectral analysis, the contrast agent is used to enhance differences in the light that is transmitted from normal and pathological tissues.
Spectral analysis offers the prospect of at least partially-automated diagnosis of tissue using a classification algorithm. However, examinations using spectral analysis may be adversely affected by glare, shadow, or the presence of blood or other obstruction, rendering an indeterminate diagnosis. Some artifacts may not be detectable by analysis of the spectral data alone; hence, erroneous spectral data may be inseparable from valid spectral data.
There exists a general need for more accurate spectral analysis methods for diagnosing tissue. More specifically, there is a need to reduce the inaccuracy of tissue classification algorithms due to erroneous spectral data.